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Saginaw Bay and western Lake Erie basin (WLEB) are eutrophic catchments in the Laurentian Great Lakes that experience annual, summer-time cyanobacterial blooms. Both basins share many features including similar size, shallow depths, and equivalent-sized watersheds. They are geographically close and both basins derive a preponderance of their nutrient supply from a single river. Despite these similarities, the bloom phenology in each basin is quite different. The blooms in Saginaw Bay occur at the same time and place and at the same moderate severity level each year. The WLEB, in contrast, exhibits far greater interannual variability in the timing, location, and severity of the bloom than Saginaw Bay, consistent with greater and more variable phosphorus inputs. Saginaw Bay has bloom biomass that corresponds to relatively mild blooms in WLEB, and also has equivalent phosphorus loads. This result suggests that if inputs of P into the WLEB were reduced to similarly sized loads as Saginaw Bay the most severe blooms would be abated. Above 500 t P input, which occur in WLEB, blooms increase non-linearly indicating any reduction in P-input at the highest inputs levels currently occurring in the WLEB, would yield disproportionately large reductions in cyanobacterial bloom intensity. As the maximum phosphorus loads in Saginaw Bay lie just below this inflection point, shifts in the Saginaw Bay watershed toward greater agriculture uses and less wetlands may substantially increase the risk of more intense cyanobacterial blooms than presently occur., (Copyright © 2021. Published by Elsevier B.V.)

Using satellite imagery to quantify the spatial patterns of cyanobacterial toxins has several challenges. These challenges include the need for surrogate pigments - since cyanotoxins cannot be directly detected by remote sensing, the variability in the relationship between the pigments and cyanotoxins - especially microcystins (MC), and the lack of standardization of the various measurement methods. A dual-model strategy can provide an approach to address these challenges. One model uses either chlorophyll-a (Chl-a) or phycocyanin (PC) collected in situ as a surrogate to estimate the MC concentration. The other uses a remote sensing algorithm to estimate the concentration of the surrogate pigment. Where blooms are mixtures of cyanobacteria and eukaryotic algae, PC should be the preferred surrogate to Chl-a. Where cyanobacteria dominate, Chl-a is a better surrogate than PC for remote sensing. Phycocyanin is less sensitive to detection by optical remote sensing, it is less frequently measured, PC laboratory methods are still not standardized, and PC has greater intracellular variability. Either pigment should not be presumed to have a fixed relationship with MC for any water body. The MC-pigment relationship can be valid over weeks, but have considerable intra- and inter-annual variability due to changes in the amount of MC produced relative to cyanobacterial biomass. To detect pigments by satellite, three classes of algorithms (analytic, semi-analytic, and derivative) have been used. Analytical and semi-analytical algorithms are more sensitive but less robust than derivatives because they depend on accurate atmospheric correction; as a result derivatives are more commonly used. Derivatives can estimate Chl-a concentration, and research suggests they can detect and possibly quantify PC. Derivative algorithms, however, need to be standardized in order to evaluate the reproducibility of parameterizations between lakes. A strategy for producing useful estimates of microcystins from cyanobacterial biomass is described, provided cyanotoxin variability is addressed., (Published by Elsevier B.V.)

An operational forecast system for harmful algal blooms (HABs) in southwest Florida is analyzed for forecasting skill. The HABs, caused by the toxic dinoflagellate, Karenia brevis, lead to shellfish toxicity and to respiratory irritation. In addition to predicting new blooms and their extent, HAB forecasts are made twice weekly during a bloom event, using a combination of satellite derived image products, wind predictions, and a rule-based model derived from previous observations and research. These forecasts include: identification, intensification, transport, extent, and impact; the latter being the most significant to the public. Identification involves identifying new blooms as HABs and is validated against an operational monitoring program involving water sampling. Intensification forecasts, which are much less frequently made, can only be evaluated with satellite data on mono-specific blooms. Extent and transport forecasts of HABs are also evaluated against the water samples. Due to the resolution of the forecasts and available validation data, skill cannot be resolved at scales finer than 30 km. Initially, respiratory irritation forecasts were analyzed using anecdotal information, the only available data, which had a bias toward major respiratory events leading to a forecast accuracy exceeding 90%. When a systematic program of twice-daily observations from lifeguards was implemented, the forecast could be meaningfully assessed. The results show that the forecasts identify the occurrence of respiratory events at all lifeguard beaches 70% of the time. However, a high rate (80%) of false positive forecasts occurred at any given beach. As the forecasts were made at half to whole county level, the resolution of the validation data was reduced to county level, reducing false positives to 22% (accuracy of 78%). The study indicates the importance of systematic sampling, even when using qualitative descriptors, the use of validation resolution to evaluate forecast capabilities, and the need to match forecast and validation resolutions.